Administrators of computer systems often make decisions about selecting control settings for computer systems. For example, a server environment may share common system resources (CPU, memory, disk space) between many computing applications, each with differing priority levels. The demands of the applications may change over time of day (e.g., backups) or day of the month (e.g., end-of-month financial data runs). Oftentimes an administrator of the server may wish to select different values for control variables that control the system for different periods of time. Examples of such control variables may include priorities for the various applications, caching policies, memory and/or storage allotment, etc. to allow for efficient execution of the various workload runs on the server over the different periods of time. However, it may be difficult for an administrator to select values for the many different control variables. It may be especially difficult for the administrator to know if a particular selection of values for the control variables is collectively superior to other selections in providing improved performance. This is particularly true when the system utilizes many control variables, creating a problem space having a high dimensionality.
In various existing systems, an administrator may attempt to determine one or more values by running test processes, looking at results, changing a control variable value, and running the test processes again. While this may, in some scenarios, aid an administrator in selecting some control variable values, there administrator may not have great confidence that there are not other values that would provide even better performance results.